Data Scientist

• Define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program
• Provide technical leadership across enterprise data science and AI/ML initiatives
• Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production
• Lead MLOps implementation for model lifecycle management, automation, observability, and continuous improvement
• Apply NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis
• Design and support event-driven analytics and real-time/streaming ML pipelines
• Collaborate with data engineers, architects, application teams, and subject matter experts to integrate AI/ML capabilities into enterprise systems and workflows
• Support system-of-systems integrations across multiple systems, vendors, contractors, and interdependent platforms
• Establish AI governance frameworks for fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework
• Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables
• Support DataOps and Agile data science practices, including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery
• Ensure analytics solutions align with enterprise security, privacy, and compliance requirements
• Improve data quality, validation, accessibility, and operational analytics reliability
• Present findings, recommendations, and technical approaches to executive leadership and stakeholders
• Mentor data scientists and analytics teams and promote organizational best practices

Lead Data Scientist

• Build Reframe Data’s data licensing business and develop strategic partnerships in collaboration with the founder
• Lead enterprise sales to data and intelligence platforms, financial information providers, institutional investors, AI companies, publishers, and pharmaceutical and biotechnology companies
• Manage the full commercial cycle from prospect identification and qualification through contract negotiation, renewal, and expansion
• Work with the founder on the first 20–30 priority accounts
• Establish repeatable sales practices and inform development of a broader commercial team
• Identify and pursue buyers for political, scientific, and pharmaceutical data, knowledge graphs, news, and related platform and service offerings
• Develop relationships with decision-makers in data acquisition, product, research, AI, business development, and procurement
• Translate customer requirements into evaluations, paid pilots, and annual licensing agreements
• Lead negotiations covering usage rights, redistribution, AI use, historical data, and expansion
• Develop pricing balancing customer value, delivery requirements, and recurring revenue
• Coordinate with technical and domain teams on data samples, coverage, quality, entity resolution, and integration requirements
• Manage pipeline development, forecasting, renewals, and account expansion
• Translate customer feedback into product recommendations aligned with commercial priorities and the product roadmap

Head of Data Partnerships – Sales

• Translate mission and business questions into analytical approaches with government stakeholders and subject matter experts
• Perform exploratory and statistical analysis, predictive modeling, forecasting, scenario analysis, and feature engineering using governed enterprise data
• Develop, validate, and maintain models and AI-enabled analytical capabilities
• Document assumptions, definitions, and data lineage
• Establish whether outputs are accurate, explainable, and fit for the supported decision
• Build analytical datasets using Databricks, BigQuery, Python, and SQL
• Work with the data engineering team where production pipelines are required
• Take ownership of existing analytical products, reports, and stakeholder relationships
• Sustain and improve existing capabilities while building new ones
• Deliver visualizations, dashboards, models, and written analysis for technical and non-technical audiences
• Present findings to stakeholders and leadership

Senior Data Scientist

• Collaborate with meteorologists at NCEP, NWS field offices, academia, and outside partners to develop forecast tools for short to medium range time frames regarding hazardous weather
• Use scientific and technical meteorological expertise to develop tools for determination of hazard information
• Contribute to formal scientific publications and/or attend off-site conferences, symposia, and hazardous-weather-related outreach events
• Collaborate to transition forecaster tools developed at WPC and elsewhere within NOAA into operations at WPC and other NOAA offices
• Develop web applications using modern industry languages and tools such as GIS, Python, and PHP
• Develop training materials to transition new tools and techniques into operations
• Design and train machine learning models to identify precursors to hazardous weather events
• Implement AI-based anomaly detection systems to identify errors or gaps in real-time forecast data
• Provide enhancements to current forecaster tools and develop new services and forecaster tools
• Apply neural networks to enhance high-resolution numerical weather prediction models
• Identify pertinent satellite, land use, and meteorological datasets and develop forecaster tools for web pages or forecaster platforms
• Leverage AI-driven data pipelines to automate ingestion and preprocessing of vast meteorological datasets
• Perform related duties as assigned

Meteorologist, Data Scientist

• Perform full lifecycle engineering processes including research, analysis, design, development, unit testing, Verification and Validation (V&V) integration testing, training, deployment, documentation, and recommendations to the Government for HawkeyeFlight software
• Develop and deploy machine learning, natural language processing (NLP), predictive analytics, and statistical learning model (SLM) solutions
• Design, develop, train, validate, and deploy machine learning and statistical learning models
• Develop NLP solutions for maintenance documentation, technical publications, operational reports, and other unstructured data
• Create predictive and prescriptive analytics algorithms for maintenance forecasting, reliability analysis, readiness reporting, and anomaly detection
• Perform data cleansing, normalization, enrichment, quality assessment, and validation across enterprise data sources
• Develop feature engineering methodologies to improve machine learning model performance and accuracy
• Build and maintain data pipelines for ingestion, transformation, model training, and analytics delivery
• Conduct exploratory data analysis and statistical evaluations to identify trends, correlations, and operational insights
• Collaborate with software developers and systems engineers to operationalize AI/ML solutions in production environments
• Support migration and modernization involving cloud-native architectures and secure government-hosted environments
• Create analytical dashboards, visualizations, reports, and executive briefings
• Participate in Agile activities including requirements analysis, sprint planning, testing, demonstrations, and release readiness reviews
• Support model governance, traceability, performance validation, and continuous improvement
• Ensure compliance with data governance, cybersecurity, and government requirements
• Support modernization and advancement of data analytics capabilities for a U.S. Navy aviation sustainment program